… In this work, the deep wavelet auto-encoder model is used for training and testing using …
Database 2012 consists of 500 MR brain images in the training stage and 500 MR images, …

… auto encoder is typically robust and have unsupervised feature learning ability, whereas
wavelet … about the technique was worked to detect brain tumors using brain MRI images. An …

… This section used the deep wavelet auto-encoder model to process MR dataset and the
segmentation method described in Figure 5 and the classification method, as shown in Figures 6 …

… [20] Spatial updated deep auto-encoder (SDAE) used feature … to the wavelet coefficient
using a bi-orthogonal wavelet … -DA CNN model to detect brain tumors from the MRI images. …

… In the MR images, the brain tumor segmentation is the current … from the GLCM, DWT and
Gabor wavelet. For the feature set … However, this auto-encoder cannot classify. Therefore, this …

… used to train the deep learning model. Segmentation using supervised Auto-encoder (AE) is
… ) [23] offered a new deep learning algorithm for tumor classification in MR images. To obtain …

… The standard deviation of noise in an MRI image is used to … Wavelet filter is used to denoise
the images with Gaussian … to a wavelet transform before undergoing an inverse wavelet …

… segmentation of brain tumors in MRI images and uses the technique of weighted fuzzy factor
based on kernel metrics. Here, a deep auto encoder (DAE) … Berkeley wavelet transformation …

… of CNN and SVM in the classification of cerebral tumour images [7]'' … The categorization of
brain tumours using MR images is … for obtaining MRI pictures of a patient's brain. Gaussian, a …

… brain MR image segmentation, this research provides a hybrid deep learning-based brain
tumor diagnosis and classification. … It was a mix of the auto-encoder, a fundamental feature …